Context Engine MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it uses Augment's LLM API (searchAndAsk), requires network access and authentication (auggie login), and automatically searches for relevant code context. It does not mention rate limits or error handling, but covers essential operational aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose. The example and additional details are useful, but the last sentence could be more concise. Overall, most sentences earn their place without significant waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (AI-powered enhancement with codebase integration), no annotations, and no output schema, the description is fairly complete. It explains the process, requirements, and provides an example, though it could benefit from mentioning output format or error cases to reach full completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema by mentioning the parameter in the example ('prompt: "fix the login bug"'), but does not provide additional semantics like constraints or usage tips beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('transform', 'enhance') and resources ('prompt'), distinguishing it from siblings like 'get_context_for_prompt' or 'semantic_search'. It explicitly defines the transformation from simple to detailed prompts with codebase context, avoiding tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (to transform simple prompts into detailed ones with codebase context) and includes an example. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'get_context_for_prompt' for raw context retrieval.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.